This type of in-database processing reduces the time needed to build, execute and deploy powerful predictive models. It also increases the utilization of the enterprise data warehouse or relational database to reduce costs and improve the data governance that is required for successful analytic applications.

Benefits

In-database analytics reduce, or eliminate, the need to move massive data sets between a data warehouse and the SAS environment or other analytical data marts for multipass data preparation and compute-intensive analytics.

The massively parallel architecture of data warehouses is useful for processing larger, more complex information sets. Modelers can easily add new sets of variables if model performance degrades or changes are needed for business reasons.

Achieve faster time to results by building, updating and deploying models more quickly.

SAS Analytics Accelerator for Teradata enables analytical processing to be pushed down to the database or data warehouse, shortening the time needed to build and deploy predictive models. It also reduces the latency and complexity associated with the model development process. Analytics professionals have fast access to up-to-date, consistent data and increased processing power. This delivers faster time to results and provides better insights for improved business decision making.